Many fields encounter problems associated with perceptually tuning a system. For example, in perceptually tuning or “fitting” a hearing aid, antiquated methods subjected a single sensorineurally impaired user to many and various audio-related settings of their hearing aid and, often via technical support from an audiologist, individually determined the preferred settings for that single user. This approach, however, has proven itself lacking in universal applicability.
Thus, prescriptive fitting formulas have evolved whereby large numbers of users can become satisfactorily fit by adjusting the same hearing aid device. With the advent of programmable hearing aids, this approach has become especially more viable. This approach is, however, still too general because individual preferences are often ignored. There currently exists no accepted selection strategy that provides a structured and efficient approach to incorporating individual preferences into hearing aid fittings.
In one particular hearing aid fitting selection strategy, paired comparisons were used. In this strategy, users were presented with a choice between two actual hearing aids from a large set of hearing aids and asked to compare them in an iterative round robin, double elimination tournament or modified simplex procedure until one hearing aid “winner” having optimum frequency-gain characteristics was converged upon. These uses of paired comparisons, however, are extremely impractical in time and financial resources. Moreover, such strategy cannot easily find implementation in an unsupervised home setting by an actual hearing aid user.
In a more recent, and very limited selection strategy, genetic algorithms were blended with user input to achieve a hearing aid fitting. As is known, and as its name implies, genetic algorithms, first introduced by John H. Holland, are a class of algorithms modeled upon living organisms' ability to ensure their evolutionary success via natural selection. In natural selection, the fittest organisms survive while the weakest are killed off. The next generation of organisms (children) are, thus, offspring of the fittest previous generation (parents). The algorithms also provide for mutations as insurance against the development of a relatively unchanging population incapable of continued evolution.
In breeding children or offspring in a genetic algorithm, “crossover” operators are applied to parent genes. In essence, two parent bit strings (ones and zeroes, for example) from the algorithm are crossed at a crossover point and the children are given attributes of each parent. Mutation operators are also applied to a relatively smaller number of parent bit strings, typically by replacing ones with zeroes and vice versa. Both crossover and mutation closely model biological behavior where parent chromosomes line up and crossover thereby swapping portions of their genetic code or become mutated.
The determination of which children are the results of which parents, how many children are produced, how many children survive, how long parents survive, how many mutations per children are created and other similar algorithm manipulations are functions of each particular genetic algorithm and vary, probably, as widely as the number of genetic algorithms in use.
In this particular hearing aid selection strategy using genetic algorithms, human subjects were asked to rank 20 hearing selections on a scale of 1 to 5. Then, through a series of genetic algorithm computations, a winning hearing selection was converged upon.
With absolute scaling approaches of this type, however, humans are generally not able to maintain the same response criteria over such a wide number of listening trials. For example, what a subject might record as a 2 for the first selection might not be the same 2 recorded for the twentieth selection. In other words, the scaling makes the comparison selection too complex. Moreover, and as with all hearing aid fitting selection strategies, this approach is unrealistic for hearing aid users to implement in their home in an unsupervised setting.
In a broader setting, genetic algorithms have also seen application in other perceptual tuning environments. For example, they have been used to (interactively with human subjects) tune simulated automobile wind noise to the subject's satisfaction and to successfully fit head-related transfer functions. These activities, like hearing aid fittings, take place in research settings and cannot, even if it were desirable, be readily performed in unsupervised field settings.
In a still broader setting, some genetic algorithm operators (crossover and mutation), have typically ineffectively evolved an organisms' population because of quickness, slowness, unstableness or some other poorly performing process in the operators. This is because the operators themselves typically operate directly on bit strings or directly on parameters having a wide, varied and non-linear range.
Accordingly, the art needs a better and more simple selection strategy for fitting or tuning hearing aids to individual users' preferred settings. Preferably, it needs an unsupervised field setting implementation. In a broader setting, the art needs better genetic algorithms for perceptually tuning a system having many interacting parameters. Still even more broadly, the art needs better genetic algorithm operators that serve to better evolve populations.